We have been made aware of ISGC 2024 - a particular opportunity each year to gain insights & present in the fields of Cloud/Grid Computing and High Perfomance Computing (HPC) across domains. The conference features a broad worldwide audience. NFDI4Earth Contact: Stephan Hachinger (LRZ). What follows is the CfA from the conference organisers:

We cordially invite and welcome your participation in the International Symposium on Grids and Clouds (ISGC) 2024!

Promoting the open data/open science collaboration between Asia Pacific region and the world, the Symposium offers an excellent opportunity to learn from the latest achievement from Europe, America and Asia. The goal of ISGC is to create a face-to-face venue where individual communities and national representatives can present and share their contributions to the solutions of global challenges.





(1)  PHYSICS AND ENGINEERING APPLICATIONS Submissions should report on research for physics and engineering applications exploiting grid, cloud, or HPC services, applications that are planned or under development, or application tools and methodologies.

Topics of interest include: (1) Data analysis and application including the use of ML/DL and quantum calculation algorithms; (2) Management of distributed data; (3) Performance analysis and system tuning; (4) Workload scheduling; (5) Management of an experimental collaboration as a virtual organization, in particular learning from the COVID-19 pandemic; (6) Expectations for the evolution of computing models drawn from recent experience handling extremely large and geographically diverse datasets, and (7) Expectations for the evolution of computing operations, etc towards the carbon neutral.

(2)  HEALTH & LIFE SCIENCES (INCLUDING PANDEMIC PREPAREDNESS APPLICATIONS) During the last decade, research in Biomedicine and Life Sciences has dramatically changed thanks to the continuous developments in High Performance Computing and highly Distributed Computing. The recent pandemic caused by Sars-CoV2 has clearly demonstrated the critical role of e-Infrastructures such high performance, high throughput and clouds infrastructure, but also of big-data and machine learning solutions to support the worldwide efforts to fight this pandemic.

This track aims at discussing problems, solutions and application examples in the fields of health and life sciences, with a particular focus on non-technical end users. We invite in particular submissions concentrating on applications of pandemic response and preparedness in the areas of e.g. Drug discovery, Vaccine design, Structural biology, Bioinformatics, Medical imaging, Epidemiological studies and other Public health applications. Submissions should ideally highlight how different e-Science / e-Infrastructure solutions are being applied to fight / prepare for pandemics.

(3)  EARTH & ENVIRONMENTAL SCIENCES & BIODIVERSITY APPLICATIONS Earth system and environmental sciences go far beyond investigating physical sub-systems of our planet: increasingly, we develop an understanding of the Earth as a single, highly complex, coupled physical system with living and dead organisms. For this purpose, information technology is widely used, for tasks from data taking and analysis to modelling of large parts of the earth system. Climate change has made research in this field mandatory, and supercomputing are getting involved in addressing the mitigation of natural hazards - in particular in Asia. This t centres rack as a part of ISGC 2024 not only invites contributions from the classical simulation or data analysis sectors: it encourages presentations on works that involve artificial intelligence (AI). Thus, we reflect that earth system and environmental sciences have been considerably enriched by AI techniques, in fields from weather-model downscaling over anomaly detection in measurements to surrogate models in digital twins of the earth system. Projects that emphasize open science, open/FAIR data, and effective communication with stakeholders are particularly encouraged to submit their work.

(4)  SOCIAL SCIENCES, ARTS & HUMANITIES (SSAH) APPLICATIONS Disciplines across the Social Sciences, Arts and Humanities (SSAH) have critically engaged with technological innovations such as grid- and cloud computing, and, most recently, various data analytic technologies. The increasing availability of data, ranging from social media text data to consumer big data has led to an increasing interest in analysis methods such as natural language processing, multilingualism and (semi-)automatic AI-powered translations, social network analysis, usage data analysis, machine learning and text mining, and data sharing. These developments pose challenges as well as opening up a world of opportunities. Members of the SSAH community have been at the forefront of discussions about the impact that novel forms of data, novel computational infrastructures and novel analytical methods have for the pursuit of science endeavours and our understanding of what science is and can be.

The ISGC 2024 SSAH track invites papers and presentations covering applications demonstrating the opportunities of new technologies or critically engaging with their methodological implications in the Social Sciences, Arts and Humanities. Innovative application of analytical tools or international data space for survey and usage data, social media data, and government (open) data are welcomed. We also invite contributions that critically reflect on the following

subjects: (1) the impact that ubiquitous and mobile access to information and communication technologies have for society more generally, especially around topics such as smart cities, civic engagement, and digital journalism; (2) philosophical and methodological reflections on the development of the techniques and the approaches by which data scientists use to pursue knowledge.

(5)  VIRTUAL RESEARCH ENVIRONMENT (INCLUDING TOOLS, SERVICES, WORKFLOWS, PORTALS, … ETC.) Virtual Research Environments (VRE) provide an intuitive, easy-to-use and secure access to (federated) computing resources for solving scientific problems, trying to hide the complexity of the underlying infrastructure, the heterogeneity of the resources, and the interconnecting middleware. Behind the scenes, VREs comprise tools, middleware and portal technologies, workflow automation as well a security solutions for layered and multifaceted applications. Topics of interest include but are not limited to: (1) Real-world experiences building and/or using VREs to gain new scientific knowledge; (2) Middleware technologies, tools, services beyond the state-of-the-art for VREs; (3) Science gateways as specific VRE environments, (4) Innovative technologies to enable VREs on arbitrary devices, including Internet-of-Things; and (5) One-step-ahead workflow integration and automation in VREs.


The rapid growth of the data available to scientists and scholars – in terms of Velocity and Variety as well as sheer Volume – is transforming research across disciplines. Increasingly these data sets are generated not just through experiments, but as a byproduct of our day-to-day digital lives. This track explores the consequences of this growth, and encourages submissions relating to two aspects in particular - firstly, the conceptual models and analytical techniques required to process data at scale; secondly, approaches and tools for managing and creating these digital assets throughout their lifecycle.

Additionally, a significant additional dimension is the automated generation and provisioning of metadata, either from simulated data such as Digital Twins or from experiments that produce vast amounts of data beyond manual annotation capacity. The automation of metadata creation and their availability in searchable catalogues is crucial for aligning with the FAIR Data Principles, ensuring data is findable and reusable. This process is also pivotal in making data usable for machine-driven applications, notably in AI training scenarios."


Networking and the connected e-Infrastructures are becoming ubiquitous. Ensuring the smooth operation and integrity of the services for research communities in a rapidly changing environment are key challenges. This track focuses on the current state of the art and recent advances in these areas: networking, infrastructure, operations, and security. The scope of this track includes advances in high-performance networking (software defined networks, community private networks, the IPv4 to IPv6 transition, cross-domain provisioning), the connected data and compute infrastructures (storage and compute systems architectures, improving service and site reliability, interoperability between infrastructures, data centre models), monitoring tools and metrics, service management (ITIL and SLAs), and infrastructure/systems operations and management.

Also included here are issues related to the integrity, reliability, and security of services and data: developments in security middleware, operational security, security policy, federated identity management, community management, and lessons learned from operations during the COVID-19 pandemic. Submissions should address solutions in at least one of these areas.


This track will focus on the development of cloud infrastructures and on the use of cloud computing and virtualization technologies in large-scale (distributed) computing environments in science and technology. We solicit papers describing underlying virtualization and "cloud" technology including integration of accelerators and support for specific needs of AI/ML and DNN, scientific applications and case studies related to using such technology in large scale infrastructure as well as solutions overcoming challenges and leveraging opportunities in this setting. Of particular interest are results exploring the usability of virtualization and infrastructure clouds from the perspective of machine learning and other scientific applications, the performance, reliability and fault-tolerance of solutions used, and data management issues. Papers dealing with the cost, price, and cloud markets, with security and privacy, as well as portability and standards, are also most welcome.


The classical simulation-oriented computing is nowadays complemented by the machined deep learning approaches. This requires novel approaches to build and integrate high performance computing infrastructures, combining supercomputers, clouds, and specialized accelerator and artificial intelligence hardware. The integration of these different systems, often provided by different owners and different location, requires new ideas for distribution and federation.

This track solicits recent research and development achievements and best practices in building and exploiting these converging high performance infrastructures or their components. The topics of interest include, but are not limited to the followings:  (1) Building and use of modern high performance computing systems, including special support for AI and DNN in particular; (2) Experiences, use cases and best practices on the development and operation of large-scale heterogeneous applications; (3) Integration and interoperability to support coordinated federated use of different e-infrastructures (supercomputers, accelerated clouds, …) and their building blocks; (4) Use of virtualization techniques and containers to support access to and portability across different heterogeneous systems; (5) Performance of different applications on these integrated high performance infrastructures.


During the last decade, Artificial Intelligence (AI) and statistical learning techniques have started to become pervasive in scientific applications, exploring the adoption of novel algorithms, modifying the design principles of application workflows, and impacting the way in which grid and cloud computing services are used by a diverse set of scientific communities. This track aims at discussing problems, solutions and application examples related to this area of research, ranging from R&D activities to production-ready solutions. Topics of interests in this track include: AI-enabled scientific workflows; novel approaches in scientific applications adopting machine learning

(ML) and deep learning (DL) techniques; cloud-integrated statistical learning as-a-service solutions; anomaly detection techniques; predictive and prescriptive maintenance; experience with MLOps practices; AI-enabled adaptive simulations; experience on ML/DL models training and inference on different hardware resources for scientific applications.

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